Unit 2.2 Data Compression, Images
Lab will perform alterations on images, manipulate RGB values, and reduce the number of pixels. College Board requires you to learn about Lossy and Lossless compression!
- Enumerate "Data" Big Idea from College Board
- Image Files and Size
- Displaying images in Python Jupyter notebook
- Reading and Encoding Images (2 implementations follow)
- Data Structures, Imperative Programming Style, and working with Images
- Data Structures and OOP
- Additionally, review all the imports in these three demos. Create a definition of their purpose, specifically these ...
- Hacks!!!
- 5 Mistake
- 4 Mistake
Enumerate "Data" Big Idea from College Board
Some of the big ideas and vocab that you observe, talk about it with a partner ...
- "Data compression is the reduction of the number of bits needed to represent data"
- "Data compression is used to save transmission time and storage space."
- "lossy data can reduce data but the original data is not recovered"
- "lossless data lets you restore and recover"
The Image Lab Project contains a plethora of College Board Unit 2 data concepts. Working with Images provides many opportunities for compression and analyzing size.
Image Files and Size
Here are some Images Files. Download these files, load them into
imagesdirectory under _notebooks in your Blog.
Describe some of the meta data and considerations when managing Image files. Describe how these relate to Data Compression ...
- File Type, PNG and JPG are two types used in this lab
- Size, height and width, number of pixels
- Visual perception, lossy compression
Displaying images in Python Jupyter notebook
Python Libraries and Concepts used for Jupyter and Files/Directories
IPython
Support visualization of data in Jupyter notebooks. Visualization is specific to View, for the web visualization needs to be converted to HTML.
pathlib
File paths are different on Windows versus Mac and Linux. This can cause problems in a project as you work and deploy on different Operating Systems (OS's), pathlib is a solution to this problem.
- What are commands you use in terminal to access files?
ls, cd, pwd
- What are the command you use in Windows terminal to access files?
dir. ren, chdir
- What are some of the major differences?
Windows vs Linux :directory listing (Windows - dir, Linux - ls), rename a file (Windows - ren, Linux - mv), returns current directory location (Windows - chdir, Linux - pwd), both change to current directory is cd Provide what you observed, struggled with, or leaned while playing with this code.
- Why is path a big deal when working with images?
It helps to identify the location of the image file on a computer or server so that you can open, edit, and display it. A path includes the name of the directory/folder and anysubdirectories or subfolders that the file is located in. This information is important because it helps to locate the image file and retrieve it when needed.
- How does the meta data source and label relate to Unit 5 topics?
Unit 5 is where students learn about the development of computer programs that process and analyze data. This includes working with text, images, and video, and understanding how to access and manipulate that data. When it comes to images, metadata refers to the information embedded in an image file, like the date/time the image was taken, the camera settings used to capture the image, and the location where the image was taken. This data can be accessed and analyzed using programming techniques. Labeling is when you assign descriptive tags/labels to images to help identify and classify them. This is typically used in ML and AI where image recognition can classify and identity different objects. Like a robot being able to tell the difference between a cat and a dog.
- Look up IPython, describe why this is interesting in Jupyter Notebooks for both Pandas and Images?
IPython (Interactive Python) is a command shell for interactive computing. It is now known as Jupyter Notebook. Jupyter Notebook is a preferable platform for pandas and images because it is a way to put data, code, visualizations, and documentation all in one single notebook. You can store massive amounts of metadata from images and format it into pandas dataframe to later change it
from IPython.display import Image, display
from pathlib import Path # https://medium.com/@ageitgey/python-3-quick-tip-the-easy-way-to-deal-with-file-paths-on-windows-mac-and-linux-11a072b58d5f
# prepares a series of images
def image_data(path=Path("images/"), images=None): # path of static images is defaulted
if images is None: # default image
images = [
{'source': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds-impression.png"},
{'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "lassen-volcano.jpg"},
{'source': "Emma Shen", 'label': "Smiley Face", 'file': "smileyface.png"}
]
for image in images:
# File to open
image['filename'] = path / image['file'] # file with path
return images
def image_display(images):
for image in images:
display(Image(filename=image['filename']))
# Run this as standalone tester to see sample data printed in Jupyter terminal
if __name__ == "__main__":
# print parameter supplied image
green_square = image_data(images=[{'source': "Internet", 'label': "Green Square", 'file': "green-square-16.png"}])
image_display(green_square)
# display default images from image_data()
default_images = image_data()
image_display(default_images)
Reading and Encoding Images (2 implementations follow)
PIL (Python Image Library)
Pillow or PIL provides the ability to work with images in Python. Geeks for Geeks shows some ideas on working with images.
base64
Image formats (JPG, PNG) are often called *Binary File formats, it is difficult to pass these over HTTP. Thus, base64 converts binary encoded data (8-bit, ASCII/Unicode) into a text encoded scheme (24 bits, 6-bit Base64 digits). Thus base64 is used to transport and embed binary images into textual assets such as HTML and CSS.
- How is Base64 similar or different to Binary and Hexadecimal?
Binary is most basic representation of data using only 0 and 1 to represent each bit of information while Hexadecimal is a base-16 numbering system that uses 16 digits (0-9 and A-f) to represent a byte of information. They are both numerical systems to represent data in computers. However, Base64 is a method for encoding binary data using 64 characters that are safe for use in email and other text-based communication channels. It has the most variety in being able to store numbers, letters, characters, AND symbols. It works by breaking the input data into blocks of three bytes, and then encoding each block as four characters from a pre-defined set of 64 characters. The resulting encoded text can then be sent as ASCII text.
- Translate first 3 letters of your name to Base64:
emm --> ZW1t
numpy
Numpy is described as "The fundamental package for scientific computing with Python". In the Image Lab, a Numpy array is created from the image data in order to simplify access and change to the RGB values of the pixels, converting pixels to grey scale.
io, BytesIO
Input and Output (I/O) is a fundamental of all Computer Programming. Input/output (I/O) buffering is a technique used to optimize I/O operations. In large quantities of data, how many frames of input the server currently has queued is the buffer. In this example, there is a very large picture that lags.
- Where have you been a consumer of buffering?
I see buffering/loading screens everywhere on my phone and computer. To make up for loading time and to process the input and prepare for display of output, buffering screens are important to keep user entertained and know that request is still processing. Below are two ways that Synergy StudentVue uses buffering
- From your consumer experience, what effects have you experienced from buffering?
The more data is being loaded the longer the buffer is
- How do these effects apply to images?
Larger images take more time to load. For lossy files they take a short time because the image is compressed to load faster while for lossless files they save original data and preserve the quality which causes it to load for longer time
Data Structures, Imperative Programming Style, and working with Images
Introduction to creating meta data and manipulating images. Look at each procedure and explain the the purpose and results of this program. Add any insights or challenges as you explored this program.
Manipulate images to different colors / filters
- Does this code seem like a series of steps are being performed?
Yes like imperative programming sequence
- Describe Grey Scale algorithm in English or Pseudo code?</li>
</ul>
- Initialize a new image object with the same dimensions as the input image.
- For each pixel in the input image:a. Retrieve the red, green, and blue values of the pixel. b. Calculate the average of the red, green, and blue values. c. Set the red, green, and blue values of the pixel in the new image object to the average value.
- Return the new image object.</b>
- Describe scale image? What is before and after on pixels in three images? -->
Scaling an image means changing its size, either making it larger or smaller. When an image is scaled, each pixel in the image is adjusted to a new position and/or color value, in order to create a new image with the desired size.</p>
Before scaling, each pixel in the original image has a specific position (x,y) and color value (RGB).
After scaling, the position and color value of each pixel in the scaled image will be different from its position and color value in the original image. </b>
- Is scale image a type of compression? If so, line it up with College Board terms described? --> </li>
</ul>
Scaling an image can be considered a type of compression, as it reduces the amount of data required to represent the image by changing its size.
For College Board AP CSP, it describes compression as "reducing the number of bits used to represent data in a file." This usually involves using techniques such as lossless compression algorithms, which exploit redundancy in the data to reduce its size without losing any information, or lossy compression algorithms, which selectively discard some information in order to achieve greater compression.
Scaling an image, on the other hand, only changes the size and resolution of the image, without necessarily reducing the number of bits used to represent the data in the file. </b>
</div> </div> </div>from IPython.display import HTML, display from pathlib import Path # https://medium.com/@ageitgey/python-3-quick-tip-the-easy-way-to-deal-with-file-paths-on-windows-mac-and-linux-11a072b58d5f from PIL import Image as pilImage # as pilImage is used to avoid conflicts from io import BytesIO import base64 import numpy as np # prepares a series of images def image_data(path=Path("images/"), images=None): # path of static images is defaulted if images is None: # default image images = [ {'source': "Internet", 'label': "Green Square", 'file': "green-square-16.png"}, {'source': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds-impression.png"}, {'source': "Emma Shen", 'label': "pretty", 'file': "mountain.png"} ] for image in images: # File to open image['filename'] = path / image['file'] # file with path return images # Large image scaled to baseWidth of 320 def scale_image(img): baseWidth = 320 scalePercent = (baseWidth/float(img.size[0])) scaleHeight = int((float(img.size[1])*float(scalePercent))) scale = (baseWidth, scaleHeight) return img.resize(scale) # PIL image converted to base64 def image_to_base64(img, format): with BytesIO() as buffer: img.save(buffer, format) return base64.b64encode(buffer.getvalue()).decode() # Set Properties of Image, Scale, and convert to Base64 def image_management(image): # path of static images is defaulted # Image open return PIL image object img = pilImage.open(image['filename']) # Python Image Library operations image['format'] = img.format image['mode'] = img.mode image['size'] = img.size # Scale the Image img = scale_image(img) image['pil'] = img image['scaled_size'] = img.size # Scaled HTML image['html'] = '<img src="data:image/png;base64,%s">' % image_to_base64(image['pil'], image['format']) # Create Grey Scale Base64 representation of Image def image_management_add_html_grey(image): # Image open return PIL image object img = image['pil'] format = image['format'] img_data = img.getdata() # Reference https://www.geeksforgeeks.org/python-pil-image-getdata/ image['data'] = np.array(img_data) # PIL image to numpy array image['gray_data'] = [] # key/value for data converted to gray scale # 'data' is a list of RGB data, the list is traversed and hex and binary lists are calculated and formatted for pixel in image['data']: # create gray scale of image, ref: https://www.geeksforgeeks.org/convert-a-numpy-array-to-an-image/ average = (pixel[0] + pixel[1] + pixel[2]) // 3 # average pixel values and use // for integer division if len(pixel) > 3: image['gray_data'].append((average, average, average, pixel[3])) # PNG format else: image['gray_data'].append((average, average, average)) # end for loop for pixels img.putdata(image['gray_data']) image['html_grey'] = '<img src="data:image/png;base64,%s">' % image_to_base64(img, format) # Jupyter Notebook Visualization of Images if __name__ == "__main__": # Use numpy to concatenate two arrays images = image_data() # Display meta data, scaled view, and grey scale for each image for image in images: image_management(image) print("---- meta data -----") print(image['label']) print(image['source']) print(image['format']) print(image['mode']) print("Original size: ", image['size']) print("Scaled size: ", image['scaled_size']) print("-- original image --") display(HTML(image['html'])) print("--- grey image ----") image_management_add_html_grey(image) display(HTML(image['html_grey'])) print()
Data Structures and OOP
Most data structures classes require Object Oriented Programming (OOP). Since this class is lined up with a College Course, OOP will be talked about often. Functionality in remainder of this Blog is the same as the prior implementation. Highlight some of the key difference you see between imperative and oop styles.
- Read imperative and object-oriented programming on Wikipedia
- Consider how data is organized in two examples, in relations to procedures
- Look at Parameters in Imperative and Self in OOP
Additionally, review all the imports in these three demos. Create a definition of their purpose, specifically these ...
- PIL
- numpy
- base64
from IPython.display import HTML, display from pathlib import Path # https://medium.com/@ageitgey/python-3-quick-tip-the-easy-way-to-deal-with-file-paths-on-windows-mac-and-linux-11a072b58d5f from PIL import Image as pilImage # as pilImage is used to avoid conflicts from io import BytesIO import base64 import numpy as np class Image_Data: def __init__(self, source, label, file, path, baseWidth=320): self._source = source # variables with self prefix become part of the object, self._label = label self._file = file self._filename = path / file # file with path self._baseWidth = baseWidth # Open image and scale to needs self._img = pilImage.open(self._filename) self._format = self._img.format self._mode = self._img.mode self._originalSize = self.img.size self.scale_image() self._html = self.image_to_html(self._img) self._html_grey = self.image_to_html_grey() @property def source(self): return self._source @property def label(self): return self._label @property def file(self): return self._file @property def filename(self): return self._filename @property def img(self): return self._img @property def format(self): return self._format @property def mode(self): return self._mode @property def originalSize(self): return self._originalSize @property def size(self): return self._img.size @property def html(self): return self._html @property def html_grey(self): return self._html_grey # Large image scaled to baseWidth of 320 def scale_image(self): scalePercent = (self._baseWidth/float(self._img.size[0])) scaleHeight = int((float(self._img.size[1])*float(scalePercent))) scale = (self._baseWidth, scaleHeight) self._img = self._img.resize(scale) # PIL image converted to base64 def image_to_html(self, img): with BytesIO() as buffer: img.save(buffer, self._format) return '<img src="data:image/png;base64,%s">' % base64.b64encode(buffer.getvalue()).decode() # Create Grey Scale Base64 representation of Image def image_to_html_grey(self): img_grey = self._img numpy = np.array(self._img.getdata()) # PIL image to numpy array grey_data = [] # key/value for data converted to gray scale # 'data' is a list of RGB data, the list is traversed and hex and binary lists are calculated and formatted for pixel in numpy: # create gray scale of image, ref: https://www.geeksforgeeks.org/convert-a-numpy-array-to-an-image/ average = (pixel[0] + pixel[1] + pixel[2]) // 3 # average pixel values and use // for integer division if len(pixel) > 3: grey_data.append((average, average, average, pixel[3])) # PNG format else: grey_data.append((average, average, average)) # end for loop for pixels img_grey.putdata(grey_data) return self.image_to_html(img_grey) # prepares a series of images, provides expectation for required contents def image_data(path=Path("images/"), images=None): # path of static images is defaulted if images is None: # default image images = [ {'source': "Internet", 'label': "Green Square", 'file': "green-square-16.png"}, {'source': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds-impression.png"}, {'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "lassen-volcano.jpg"}, {'source': "Emma Shen", 'label': "pretty", 'file': "mountain.png"} ] return path, images # turns data into objects def image_objects(): id_Objects = [] path, images = image_data() for image in images: id_Objects.append(Image_Data(source=image['source'], label=image['label'], file=image['file'], path=path, )) return id_Objects # Jupyter Notebook Visualization of Images if __name__ == "__main__": for ido in image_objects(): # ido is an Imaged Data Object print("---- meta data -----") print(ido.label) print(ido.source) print(ido.file) print(ido.format) print(ido.mode) print("Original size: ", ido.originalSize) print("Scaled size: ", ido.size) print("-- scaled image --") display(HTML(ido.html)) print("--- grey image ---") display(HTML(ido.html_grey)) print()
Hacks!!!
1. College Board Practice
Data Compression Quiz 3/3
Extracting info from Data Quiz 5/6
5 Mistake
Explanation: The data is determined only by image recognition by a camera. Thus, the number of bicyles passed on a particular day can be recorded (Answer Choice D). However, the calculation of a AVERAGE speed would need metadata that includes the cars distance and time.Using Programs with Data Quiz 5/6
4 Mistake
Explanation: The key to these types of questions is that I need to select TWO correct answers. Here, I chose Answer C which is wrong because this sequence of steps does not remove any entries with an unknown year, so the entry in the first row of the spreadsheet will have a year value of -1. I should've chosen Answer D instead because sorting by year will sort the spreadsheet on column C from least to greatest. Filtering by year will remove any entries with unknown years. Filtering by photographer will remove any entries with unknown photographers. Since the order of the entries is not affected by the filters, the photograph with the lowest year value will be in the first row of the spreadsheet.Binary Quiz
2. Lossy vs Lossless Image
- Lossless - compression in which the image is reduced without any quality loss Ex. A logo that can be produced in multiple places in the website and sizes
- Lossy - a process that removes some of the data from your image file, reducing the overall file size. This process is irreversible, meaning that the file information will be removed permanently.
Ex. Sending an image file to your friend over email and it asks you whether you want to send as small, medium, large, or actual size. In this situation, you can reduce the size/quality of the picture to make the file send faster.
3. Programming paradigm
-
Numpy, manipulating pixels. As opposed to Grey Scale treatment, pick a couple of other types like red scale, green scale, or blue scale. We want you to be manipulating pixels in the image.
-
Binary and Hexadecimal reports. Convert and produce pixels in binary and Hexadecimal and display.
-
Compression and Sizing of images. Look for insights into compression Lossy and Lossless. Look at PIL library and see if there are other things that can be done.
-
There are many effects you can do as well with PIL. Blur the image or write Meta Data on screen, aka Title, Author and Image size.
</div>import numpy as np from PIL import Image # Load the image image = Image.open('images/bat.png') # Add a title to the image image.info['Title'] = 'Redscaled' # Convert the image to a NumPy array img_array = np.asarray(image) # Convert the array to binary representation binary_pixels = np.unpackbits(img_array, axis=-1) # Convert the binary representation to hexadecimal hex_pixels = np.apply_along_axis(lambda x: hex(int(''.join(map(str, x)), 2))[2:].zfill(2), -1, binary_pixels) # Display the binary and hexadecimal pixels #print("Binary pixels:\n", binary_pixels) #print("Hexadecimal pixels:\n", hex_pixels) # Create a copy of the array red_img = np.copy(img_array) # Set the green and blue channels to 0, leaving only the red channel red_img[:, :, 1] = 0 red_img[:, :, 2] = 0 # Convert the NumPy array back to an image red_image = Image.fromarray(red_img) # Save the red-scale image red_image.save('images/bat.png') # Resize the image to half its original size resized_image = red_image.resize((red_image.width // 2, red_image.height // 2)) print(image.info) # Show the resized image resized_image.show()
